Introduction to Data Donation

Workshop TU Ilmenau 2026

Session 1️⃣: Welcome & Intro to Digital Trace Data

👉 Part of the SPP DFG Project Integrating Data Donations in Survey Infrastructure

Agenda

1. Intro to the workshop

2. What is digital trace data?

3. How can we collect digital traces?

Image by Hope House Press via Unsplash

Before we start: Have you requested and downloaded your Google Data? 🤔

Otherwise, use this link to request your data now: https://next.eyra.co/a/nWPJC4?p=999 - replace number after p= with random number

image of the starting page of a data donation study

1. Intro

image of lupe

Source: Image by Markus Winkler via Unsplash

Who are you?

Please raise your hand 🤚 if you ….

  • are familiar with the term digital trace data
  • have worked with APIs
  • have worked with automated content analysis
  • have worked with data donation

Who are you?

In 2-3 sentences, tell us…

  • your main research interests
  • the methods you mainly use
  • related to which theoretical questions/data you are interested in data donation as a method

About me: Valerie Hase

Professor of Digital Media and Communication

Research interests:

  • CSS (automated content analysis, digital traces, bias, data access)
  • Digital journalism, crisis communication

👉 More info: github.com/valeriehase & valerie-hase.com

A big thank you 🙌 to the organizers

Shoutout to the organizers behind this workshop

  • Max Schindler
  • Leonie Kühn

What is the goal of this workshop?

  • ✅ Understanding digital data traces as a type of data
  • ✅ Understanding data donation as a method of data access
  • ✅ Working through key steps of data donation methods (user & researcher view)
  • ✅ Discussing when (not) to use data donation studies
  • ❌ Detailed implementation (e.g., server set-up, coding data extraction scripts)

How do we communicate in this workshop?

question

How do we communicate in this workshop?

My goal is that you…

  • just ask right away if there is something you did not understand
  • keep in mind that there are not stupid questions
  • feel free to ask questions specific to your potential data donation projects!

Timetable

⏰ 10–10:15am Session 1️⃣: Welcome & Intro to Digital Traces
⏰ 10:15–11am Session 2️⃣: Data Donation Studies (Participant Perspective)
⏰ 11am–12:15pm Session 3️⃣: Data Donation Studies (Researcher Perspective)
🥖12:15–13:15pm Lunch break
⏰ 13:15–2pm Session 4️⃣: Bias in Digital Trace Data & Outro

2. What is digital trace data?

image of lupe

Source: Image by Markus Winkler via Unsplash

What is digital trace data?

Definition 💡: The recording and storing of activities on digital platforms to draw conclusions about digital and analog phenomena

  • e.g., tweets, likes, shares on social media
  • e.g., geo data (locations, movements)
  • e.g., digital payments
  • e.g., Spotify playlists

What is digital trace data?

Definition 💡: The recording and storing of activities on digital platforms to draw conclusions about digital and analog phenomena

  • e.g., tweets, likes, shares on social media

Example: Instagram Like

Screenshot of Instagram Like

What is digital trace data?

Definition 💡: The recording and storing of activities on digital platforms to draw conclusions about digital and analog phenomena

  • e.g., tweets, likes, shares on social media

Example: Instagram Like

Data behind Instagram Like

What is digital trace data?

Depending on the data collection method… (Haim & Hase, 2023; Ohme et al., 2024):

  • often fine-grained (e.g., time-stamped)
  • often longitudinal (e.g., over years, within-individual change)
  • often less reactive (e.g., less concerns about social desirability)
Exemplary trace data from YouTube searches

Which types of data does this include?

examples of digital trace data

Source: Keusch & Kreuter, 2023, p. 102

(Dis-)advantages of digital trace data

  • ✅ More fine-grained, often longitudinal measures due to timestamps
  • ✅ Partly measurement of new variables (e.g., algorithmic inference)
  • ❌ Still bias due to errors in representation and measurement
  • ❌ Implementation can be expensive and cumbersome

👉 More data does not mean better data!

Summary: What is digital trace data? 📚

  • Definition: The recording and storing of activities on digital platforms to draw conclusions about digital and analog phenomena

  • Further literature

    • Keusch & Kreuter (2021)

    • Haim & Hase (2023)

    • Ohme et al. (2024)

3. How can we collect digital traces?

image of lupe

Source: Image by Markus Winkler via Unsplash

Which methods do you know/have you used for collecting digital trace data? 🤔

Platform- and user-centric methods

the promise of data donation using a toy story meme

Platform- and user-centric methods

Platform- and user-centric methods

  • Restrictions of platform-centric methods
  • User-centric methods become more popular, given …
    • Legal frameworks enabling such studies (GDPR, DSA)
    • Presumably (!) more researcher control
    • Ethical considerations (informed consent)

Summary: How can we collect digital traces? 📚

  • Summary

    • Platform-centric methods (e.g., APIs) and user-centric methods (e.g., data donation)

    • Key differences: control over samples & measurements, legal & ethical contexts

  • Further literature

Questions? 🤔

References

Introduction to Data Donation - TU Ilmenau - Valerie Hase

Caliandro, A. (2024). Follow the user: Taking advantage of Internet users as methodological resources. Convergence: The International Journal of Research into New Media Technologies, 13548565241307569. https://doi.org/10.1177/13548565241307569
Carrière, T. C., Boeschoten, L., Struminskaya, B., Janssen, H. L., De Schipper, N. C., & Araujo, T. (2024). Best practices for studies using digital data donation. Quality & Quantity. https://doi.org/10.1007/s11135-024-01983-x
Christner, C., Urman, A., Adam, S., & Maier, M. (2022). Automated Tracking Approaches for Studying Online Media Use: A Critical Review and Recommendations. Communication Methods and Measures, 16(2), 79–95. https://doi.org/10.1080/19312458.2021.1907841
Freelon, D. (2018). Computational research in the post-API age. Political Communication, 35(4), 665–668. https://doi.org/10.1080/10584609.2018.1477506
Haim, M., & Hase, V. (2023). Computational Methods und Tools für die Erhebung und Auswertung von Social-Media-Daten. In S. Stollfuß, L. Niebling, & F. Raczkowski (Eds.), Handbuch Digitale Medien und Methoden (pp. 1–20). Springer Fachmedien Wiesbaden. https://link.springer.com/10.1007/978-3-658-36629-2_41-1
Jünger, J. (2021). A brief history of APIs. In Handbook of Computational Social Science, Volume 2 (1st ed., pp. 17–32). Routledge. https://www.taylorfrancis.com/books/9781003025245/chapters/10.4324/9781003025245-3
Keusch, F., & Kreuter, F. (2021). Digital trace data. In Handbook of Computational Social Science, Volume 1 (1st ed., pp. 100–118). Routledge. https://www.taylorfrancis.com/books/9781003024583/chapters/10.4324/9781003024583-8
Luiten, A., Hox, J., & Leeuw, E. de. (2020). Survey Nonresponse Trends and Fieldwork Effort in the 21st Century: Results of an International Study across Countries and Surveys. Journal of Official Statistics, 36(3), 469–487. https://doi.org/10.2478/jos-2020-0025
Ohme, J., Araujo, T., Boeschoten, L., Freelon, D., Ram, N., Reeves, B. B., & Robinson, T. N. (2024). Digital Trace Data Collection for Social Media Effects Research: APIs, Data Donation, and (Screen) Tracking. Communication Methods and Measures, 18(2), 124–141. https://doi.org/10.1080/19312458.2023.2181319
Parry, D. A., Davidson, B. I., Sewall, C. J. R., Fisher, J. T., Mieczkowski, H., & Quintana, D. S. (2021). A systematic review and meta-analysis of discrepancies between logged and self-reported digital media use. Nature Human Behaviour, 5(11), 1535–1547. https://doi.org/10.1038/s41562-021-01117-5
Scharkow, M. (2016). The Accuracy of Self-Reported Internet Use—A Validation Study Using Client Log Data. Communication Methods and Measures, 10(1), 13–27. https://doi.org/10.1080/19312458.2015.1118446
Schatto-Eckrodt, T. (2022). Hidden biases – The effects of unavailable content on Twitter on sampling quality. In Grenzen, Probleme und Lösungen bei der Stichprobenziehung (pp. 178–195). Halem.
Sloan, L., Jessop, C., Al Baghal, T., & Williams, M. (2020). Linking Survey and Twitter Data: Informed Consent, Disclosure, Security, and Archiving. Journal of Empirical Research on Human Research Ethics, 15(1-2), 63–76. https://doi.org/10.1177/1556264619853447
Struminskaya, B., Lugtig, P., Toepoel, V., Schouten, B., Giesen, D., & Dolmans, R. (2021). Sharing Data Collected with Smartphone Sensors. Public Opinion Quarterly, 85(S1), 423–462. https://doi.org/10.1093/poq/nfab025
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